This paper investigates the adaptation of Automatic Speaker Recognition (ASR) techniques to the pathological voice assessment (dysphonic voices). The aim of this study is to provide a novel method, suitable for keeping track of the evolution of the patient's pathology: easy-to-use, fast, non-invasive for the patient, and affordable for the clinicians. This method will be complementary to the existing ones - the perceptual judgment and the usual objective measurement (jitter, airflows...) which remain time and human resource consuming. The system designed for this particular task relies on the GMMbased approach, which is the state-of-the-art for speaker recognition. It is derived from the open source ASR tools (LIA_Spk- Det and ALIZE) of the LIA lab.Experiments conducted on a dysphonic corpus provide promising results, underlining the interest of such an approach and opening further research investigation.